Data & Analytics Services

A data strategy serves as the guiding vision supporting an organization's ability to effectively manage and harness its data resources. It establishes a direct link between strategic objectives and the organization's data assets. Furthermore, it acts as an overarching framework encompassing various domain-specific strategies, including data analytics, big data, data architecture, digital transformation, and data governance.

Within the realm of successful enterprise data and analytics (D&A) platforms, data governance and management hold pivotal roles, alongside the overarching strategy, requisite technology systems, and organizational alignment. Specifically, data governance and management involve the establishment of a consistent foundation of high-quality data. It further ensures the continuous day-to-day management and upkeep of this foundation, encompassing critical operational tasks such as data addition, modification, and sharing, as well as the establishment of user access and security protocols.

In addition, data governance and management are no longer confined to IT alone. Our observations at KPMG consistently indicate that organizations achieving the greatest success with their D&A initiatives are those that view ongoing data management as a partnership driven by the business, enabled by technology. With finance leading the charge on the business front, collaborating closely with IT and other teams, organizations can focus on high-quality data that drives tangible results. This approach avoids the pitfalls of disparate or conflicting data managed by disconnected teams. This leads to suboptimal outcomes when IT is solely responsible for governance and management.

KPMG in Vietnam assists organizations in defining, executing, and sustaining their data strategy. We actively encourage collaboration between business and IT, recognizing its pivotal role in achieving data-driven success.

Efficient metadata management stands as a cornerstone in the construction of a data-driven enterprise. It encompasses the adoption of best-practice methodologies and state-of-the-art technologies aimed at handling metadata, which essentially represents information about your data. Metadata management plays a crucial role in providing valuable insights that streamline data administration, enabling more efficient and effective data utilization. Furthermore, it empowers a wide spectrum of users, including both business and technical professionals. This is done by facilitating the location, comprehension, and accessibility of vital data essential for their specific roles. 

At its core, metadata acts as a layer of information that provides the context necessary to harness data resources optimally and unlock their full potential. To be precise, metadata aids in the discovery of data, elucidates the intricate relationships between various data components, and simplifies data utilization tracking. Additionally, it allows for a thorough assessment of the benefits and risks associated with data usage.

As exponential growth and widespread distribution of data continue to underpin numerous mission-critical operations, the strategic role of metadata management in propelling digital transformation initiatives forward becomes increasingly evident. To address these challenges effectively, KPMG has developed a comprehensive Advanced Data Management framework and methodology. This solution empowers businesses to tailor their Data Management approach, fostering greater efficiency, agility, and transparency as they embark on large-scale data-driven transformation endeavors.

Identification of data sources and their respective responsible parties is a fundamental step in data management. This process involves clearly identifying the origins of data within an organization and attributing accountability to the individuals or teams responsible for maintaining and curating that data. Establishing this link between data sources and responsible parties ensures transparency and accountability throughout the data lifecycle.

In parallel, automated or technology-supported data lineage is essential for tracking and visualizing the flow of data within complex data ecosystems. Automated data lineage tools provide a comprehensive view of how data moves from its source through various transformations and processes, all the way to its eventual consumption. This automation not only enhances data visibility but also facilitates efficient troubleshooting, compliance monitoring, and decision-making by offering insights into data's journey and its impact on business operations.

Ensuring data security and protection stands as an imperative in the safeguarding of sensitive information. The initial step involves identifying the requisite level of data protection, enabling the determination of appropriate security measures tailored to the data's sensitivity and its value to the organization. This entails categorizing data into distinct tiers, such as public, confidential, or highly sensitive, and subsequently implementing security protocols commensurate with each classification.

Beyond data protection, comprehension of the legal aspects pertaining to record retention, archiving, and deletion assumes paramount importance. Adherence to data retention laws and regulations is crucial, ensuring that organizations maintain data for stipulated durations and appropriately dispose of it when it no longer serves a purpose. This necessitates a thorough grasp of the specific legal requirements governing data storage and deletion across diverse jurisdictions and industries, guarding against potential legal consequences.

Data privacy constitutes yet another critical facet of data security. Safeguarding individuals' personal information and adhering to data privacy laws, such as GDPR or CCPA, is essential to cultivating trust and evading potential penalties. Organizations must implement robust data privacy measures encompassing consent acquisition, the provision of data subject rights, and the fortification of personal data against unauthorized access. KPMG's approach to personal data protection activities aims to assist organizations in identifying key elements of personal data protection activities by developing a strategic roadmap based on the organization's current data protection practices. The methodology provides a flexible, practical, and effective framework for organizations to manage and monitor personal data protection activities, including compliance with the General Data Protection Regulation ("GDPR") requirements of the European Union or Decree 13/2023/NĐ-CP of Vietnam.

Leveraging our profound local and global expertise in security and data protection, at KPMG, we can assist your business in adapting to compliance, competition, and commercialization. We offer innovative and tailored privacy and data protection services to meet your organization's unique needs. With KPMG's extensive experience in addressing data privacy challenges and aligning with organizational objectives, we stand ready to assist your organization in achieving its data privacy goals. We can also unlock your economic potential.

In the contemporary data landscape, the increasing reliance on big data has added complexity to both IT systems and data management. The era of cloud computing and the prevalence of dashboard-driven decision-making have heightened the importance of maintaining the integrity and quality of data repositories. These repositories serve as the foundation for accurate and timely reporting. Furthermore, this complexity is compounded by the ongoing exponential growth in data volumes, which double every two years, and the heightened frequency of reporting cycles.

Effective data quality management plays a pivotal role in transforming unstructured data into valuable insights and securing a competitive advantage. Well-maintained data repositories not only serve as the cornerstone for institutional customer reporting but are also indispensable for comprehensive regulatory reporting. Conversely, inaccurate and unreliable data can lead to erroneous conclusions, missed opportunities, and reduced operational efficiency. Given that crucial business decisions often rely on company data, these data quality issues can significantly hinder the effectiveness and efficiency of business operations.

To address these challenges, the establishment of a comprehensive Data Quality (DQ) control process is imperative. This process entails defining uniform DQ criteria and standards, particularly for risk reporting, and establishing an integrated data foundation. The implementation of a robust DQ control process, encompassing the measurement, analysis, and rectify of DQ issues with clearly delineated responsibilities, is of paramount importance. Developing a control plan for evaluating data quality at various stages of data processing ensures a consistently high level of quality. The consolidation of information into actionable insights, seamlessly integrated into existing business processes, yields enduring value.

At KPMG in Vietnam, our many years of experience in analyzing and assessing financial statements and business data positions us as experts in efficient data processing and the consolidation of actionable insights, seamlessly integrating them into established business operations. Additionally, our services encompass a thorough Technical Requirement Definition, ensuring alignment between your IT systems and your data management goals and objectives.

Data Science represents a transformative field founded on several pivotal principles. First and foremost, it revolves around the identification of insightful business use cases spanning diverse domains like customer analytics, operational efficiency, risk management, and performance optimization. By pinpointing these specific use cases, data scientists wield data's potential to guide strategic decision-making and extract meaningful insights that directly shape an organization's path to success.

A cornerstone of Data Science lies in the adept application of advanced data analytics techniques. This encompasses the utilization of sophisticated tools and methodologies to process and analyze large datasets, uncovering hidden patterns, trends, and correlations. These advanced analytical techniques empower organizations to distill actionable insights from their data, setting the stage for data-driven strategies and well-informed decision-making.

In the contemporary data-driven landscape, the integration of artificial intelligence (AI) and machine learning (ML) within Big Data assumes paramount significance. AI and ML algorithms can sift through vast data volumes, learning from them autonomously and delivering predictions or recommendations without explicit programming. This fusion of AI/ML with Big Data automation streamlines processes, augments predictive capabilities, and provides organizations with a competitive edge. This is done by harnessing the full potential of their data resources.

KPMG boasts a global network comprising over 14,000 data and technology professionals. Leveraging cutting-edge KPMG data, analytics, intelligent automation, and artificial intelligence technologies, we collaborate with clients to develop and deliver tailored solutions for their unique business requirements. Our spectrum of data science services encompasses everything from training to customized solutions. We offer trusted data and analytics solutions that facilitate fresh insights and expedite confident, data-driven business decisions.